Genome-wide loss-of-function screen using human pluripotent stem cells to study virus-host interactions for SARS-CoV-2
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019, has become a global health concern. Therefore, there is an immense need to understand the network of virus-host interactions by using human disease-relevant cells. We have thus conducted a loss-of-function genome-wide screen using haploid human embryonic stem cells (hESCs) to identify genes involved in SARS-CoV-2 infection. Although the undifferentiated hESCs are resistant to SARS-CoV-2, their differentiated definitive endoderm (DE) progenies, which express high levels of ACE2, are highly sensitive to the virus. Our genetic screening was able to identify the well-established entry receptor ACE2 as a host factor, along with additional potential novel modulators of SARS-CoV-2. Two such novel screen hits, the transcription factor MAFG and the transmembrane protein TMEM86A, were further validated as conferring resistance against SARS-CoV-2 by using CRISPR-mediated mutagenesis in hESCs, followed by differentiation of mutant lines into DE cells and infection by SARS-CoV-2. Our genome-wide genetic screening investigated SARS-CoV-2 host factors in non-cancerous human cells with endogenous ACE2 expression, providing a unique platform to identify novel modulators of SARS-CoV-2 cytopathology in human cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".